Problems with applying High Pass Gaussian Filter using Fourier transform

I wrote code using c++ and opencv 3.4.3 to apply HighPass Gaussian filter using dft, but in result i got something like this(looks like there are 2 images, original and inverted original image) (https://i.imgur.com/jtIwxq1.png) i used this algoritm:Firstly i calculate DFT, then applying mask on dft and did inverse transform. Here my code, wwhats wrong with it?

#include <opencv2/imgproc.hpp>
#include <opencv2/highgui.hpp>
#include <opencv2/opencv.hpp>
#include <iostream>

using namespace cv;
using namespace std;

void takeDFT(Mat& input, Mat& output)
{
    Mat originalComplex[2] = { input, Mat::zeros(input.size(), CV_32F) };
    Mat dftReady;
    merge(originalComplex, 2, dftReady);
    Mat dftIMG;
    dft(dftReady, dftIMG, DFT_COMPLEX_OUTPUT);
    output = dftIMG;
}

void createGaussian(Size& size, Mat& output, int uX, int uY, float sigmaX, float sigmaY)
{
    Mat temp = Mat(size, CV_32F);
    for (int r = 0; r < size.height; r++)
    {
        for (int c = 0; c < size.width; c++)
        {
            float x = ((c - uX)*((float)c - uX)) / (2.0f*sigmaX*sigmaX);
            float y = ((r - uY)*((float)r - uY)) / (2.0f*sigmaY*sigmaY);
            float value = 1 - exp(-(x + y));
            temp.at<float>(r, c) = value;
        }
    }
    normalize(temp, temp, 0.0f, 1.0f, NORM_MINMAX);
    output = temp;
    //imshow("temp", temp);
}

void invertDFT(Mat& input, Mat& output)
{
    Mat inverse;
    Mat applyGaussian;
    Mat originalComplex[2] = { input, Mat::zeros(input.size(), CV_32F) };
    split(input, originalComplex);
    Mat outputGauss;
    Size size = Size(input.cols, input.rows);
    createGaussian(size, outputGauss, input.cols/2, input.rows/2, 10, 10);
    for (int r = 0; r < originalComplex[1].rows; r++)
    {
        for (int c = 0; c < originalComplex[1].cols; c++)
        {
            originalComplex[0].at<float>(r, c) = originalComplex[0].at<float>(r, c)*outputGauss.at<float>(r, c);
        }
    }
    normalize(originalComplex[0], originalComplex[0], 0.0f, 1.0f, NORM_MINMAX);
    merge(originalComplex, 2, applyGaussian);
    dft(applyGaussian, inverse, DFT_INVERSE | DFT_REAL_OUTPUT | DFT_SCALE);
    output = inverse;
}



int main()
{
    Mat original = imread("amumu.jpg", CV_LOAD_IMAGE_GRAYSCALE);
    imshow("Original", original);
    Mat originalFloat;
    original.convertTo(originalFloat, CV_32FC1, 1.0 / 255.0);
    Mat dftOriginal;
    takeDFT(originalFloat, dftOriginal);
    Mat invertedDFT;
    invertDFT(dftOriginal, invertedDFT);
    imshow("Inverted Image", invertedDFT);
    waitKey();
}